Data Science Manager
Job description
About the role
Data Science Manager
Bank Payments
GoCardless is a global bank payment company. Over 100,000 businesses, from start-ups to household names, use our platform to collect, manage, and send bank payments through Direct Debit, real-time payments, and open banking. We process over US$130 billion annually across 30+ countries, handling recurring and one-off payments without chasing, stress, or expensive fees. Our end-to-end payment platform uses AI to improve payment success and reduce fraud, and connects with more than 350 tools businesses rely on every day.
We are headquartered in the UK, with teams and operations across North America, Europe, and Asia-Pacific.
The role
Data sits at the core of our mission. We use bank account data to deliver high-value, intelligent payment solutions for our customers, from enhancing payment success rates to driving payer fraud prevention.
As a Data Science Manager within our Payment Intelligence team, you will partner with Software Engineers, Product Managers, and Designers to turn big ideas into reality. You will own the full lifecycle of our algorithms, shaping everything from the initial concept to production-ready code that powers our global payment network.
Our stack is centered around Google Cloud Platform and Vertex AI, providing a high-performance environment for innovation. Our Data Scientists operate at the intersection of Python, SQL, and BigQuery to build and deploy high-performance models at scale.
What you'll do
- Manage and mentor a high-performing team of Data Scientists, fostering technical excellence and supporting long-term career development.
- Oversee the end-to-end lifecycle of mission-critical ML models that power real-time payment decisions.
- Shape the strategic roadmap for the Payment Intelligence space, translating complex data challenges into actionable, high-impact goals.
- Drive cross-functional impact by working closely across disciplines to build end-to-end technical solutions, from concept to production.
- Influence Senior Leadership by acting as the bridge between technical complexity and business value, communicating ML strategy to senior stakeholders.
- Design intake workflows that turn raw banking signals into clean training sets for payment models.
- Architect model builds that handle real-time fraud patterns while staying explainable and auditable.
- Guide reviews of model behavior, validating performance, bias, and drift before releases.
- Drive shipping of models into production, coordinating tests, monitoring, and rollback plans.
- Forge partnerships with product and engineering teams to align data strategy with business outcomes.
- Champion experiments that prove value quickly, then scale them using Vertex AI and BigQuery.
- Maintain rigorous documentation so decisions about models, features, and thresholds remain transparent.
- Champion code quality and data hygiene so future teams can iterate safely and confidently.
What excites you
- Driving current advancements in Data, AI, and Machine Learning within the payments space with a multidisciplinary team.
- Mentoring a high-performing team and fostering a culture of technical excellence.
- Solving complex, real-time challenges of fraud prevention and payment optimization at scale.
- Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.
What excites us
- 2+ years managing Data Scientists within complex, high-stakes domains.
- A hands-on leader comfortable diving into the codebase. You bring strong expertise in Python and SQL to oversee the full lifecycle of a model, from initial prototype to robust production deployment.
- A decisive collaborator who can navigate technical trade-offs and translate complex ML concepts for cross-functional stakeholders and leadership.
- Familiarity with complex data environments and model architectures, such as deep learning.
Key facts
Nice to have
- Experience in fintech, fraud prevention, or payments is a big plus.
Skills & tools
Google Cloud Platform, Vertex AI, Python, SQL, BigQuery.
Practical notes
Please What you'll do
- Meet the bar